RNN-based Encoder-decoder Approach with Word Frequency Estimation

By Jun Suzuki, Masaaki Nagata. 2017

13 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

Get To The Point: Summarization with Pointer-Generator Networks (2017, background): “Repetition is a common problem for sequenceto-sequence models (Tu et al., 2016; Mi et al., 2016; Sankaran et al., 2016; Suzuki and Nagata, 2016), and is especially pronounced when generating multi-sentence text (see Figure 1).”
Multi-Reward Reinforced Summarization with Saliency and Entailment (2018, background): “Coverage-based models address the latter redundancy issue (Suzuki and Nagata, 2016; Nallapati et al., 2016; See et al., 2017), but there is still a lot of scope to teach current state-of-the-art models about saliency and logical entailment.”
Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting (2018, background): “Some of the recent success in neural abstractive models include hierarchical attention (Nallapati et al., 2016), coverage (Suzuki and Nagata, 2016; Chen et al., 2016; See et al., 2017), RL based metric optimization (Paulus et al., 2018), graph-based attention (Tan et al., 2017), and the copy…”
Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation (2018, background): “The redundancy aspect was addressed by coverage models (Suzuki and Nagata, 2016; Chen et al., 2016; Nallapati et al., 2016; See et al., 2017), but we still need to teach these models about how to better detect salient information from the input document, as well as about better logicallydirected nat”
Multi-stage Pretraining for Abstractive Summarization (2019, background): “, 2017) and coverage modeling (Suzuki and Nagata, 2016; Chen et al., 2016; See et al., 2017; Gehrmann et al., 2018) are examples of these approaches.”
Double Path Networks for Sequence to Sequence Learning (2018, background): “, 2016), WFE (Suzuki and Nagata, 2017) and ConvS2S (Gehring et al.”